Robust Tracking of Nonlinear Target Motion Using Out-of-Order Sigma Point Kalman Filters
Hyukseong Kwon, Daniel J. Pack · Infotech@Aerospace 2012 · 2012
Over the past decade, the tasks of autonomous localization and tracking of mobile ground targets using cooperative, multiple small unmanned aerial vehicles (UAVs) have been gaining an increasing amount of interest among researchers in the UAV community. Robust solutions have been elusive due to a number of challenges including sudden, unpredictable route changes of targets; inaccurate computation of small platform attitudes; and limited sensor field of views. In our previous works1,2 we demonstrated the merits of the Out-Of-Order Sigma-Point Kalman Filter (O3SPKF) and the concept of Sensor Fusion Quality (SFQ) as multiple UAVs effectively tracked and located mobile ground targets moving in a linear fashion. We showed that the O3SPKF enables UAVs to optimally incorporate randomly time-delayed sensor information, also called out-of-order data, while the SFQ technique removes invalid sensor data, enhancing the target localization accuracy for linearly moving targets. In this paper we present an improved O3SPKF/SFQ method that uses both linear and circular target tracking models to geo-locate mobile targets with non-linear motions.